The evolution of deep learning and machine learning

Machine learning has laid down the fundamental groundwork for deep learning to evolve.

Shares

2016 has been the year of Artificial Intelligence (AI), and more specifically, the breakout of machine learning and deep learning becoming the big buzz words in technology. While both have gained a lot of attention this year, these techniques have been around for quite some time, but no more so than now, has it felt so promising.

Over the past few years, there has been a monumental shift in technology and how it’s being applied to everyday life. From robots to search engines, deep learning and machine learning are being raved about as the tech fuelling our new innovations, but many are left wondering what truly differentiates these two models.

Broadly speaking, both machine learning and deep learning are forms of Artificial Intelligence, the intelligence exhibited by machines using cutting-edge techniques to perform cognitive functions that we associate with intuitive learning; however, each application is unique and offers an array of benefits to the end-user, whether it’s solving unique problems for a particular business case, aiding in speech/facial recognition, speeding up web applications or protecting against breaches or hacks. While the concepts of machine learning and deep learning have been around as early as the 1960s, each model has changed drastically over the years, creating a greater divide between the two.

Machine learning: The fundamental piece to the AI puzzle

Machine learning is a type of AI that facilitates a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. For example, Facebook’s news feed utilises machine learning in an effort to personalise each individual’s feed based on what they like. The main elements of traditional machine learning software are statistical analysis and predictive analysis used to spot patterns and find hidden insights based on observed data from previous computations without being programmed on where to look.

Machine learning has truly evolved over the years by its ability to sift through complex and big data. Many may be surprised to know that they encounter machine learning applications in their everyday lives through streaming services like Netflix and social media algorithms that alert to trending topics or hashtags. While machine learning has become an integral part of processing data, one of the main differences when compared to deep learning is that it requires manual intervention in selecting which features to process, whereas deep learning does it intuitively.

Feature extraction in machine learning requires a programmer to tell the computer what kinds of things it should be looking for that will be formative in making a decision, which can be a time-consuming process. This also results in machine learning having decreased accuracy due to the element of human error during the programming process.

Deep learning: A mind of its own

Deep learning is one paradigm for performing machine learning, and the technology has become a hot focus due to the unparalleled results it has yielded in applications such as computer vision (object/face recognition), speech recognition, natural language understanding and cyber threat detection. Not to mention the handful of top companies including Google, Facebook, Baidu and Microsoft who are starting to leverage this type of technology. It will be interesting to see which new companies will begin to utilise deep learning in the future.

For starters, deep learning is an advanced, sophisticated branch of AI with predictive capabilities that is inspired by the brain’s ability to learn. Just as the human brain can identify an object in milliseconds, deep learning can mirror this instinct with nearly the same speed and precision. For instance, while many traditional computer vision modules can easily recognise any given object, the second there is a slight obstruction, the technology struggles with identification. That’s where deep learning comes into play because it is resistant to small changes and can generalise from partial data making it easy for the module to correctly identify a partially-obstructed object. Deep learning has the nimble ability to assess an object, properly digest the information and adapt to different variants.

This is the most significant improvement that deep learning provides over classical machine learning – eliminating the need for feature engineering. If you would like to use machine learning for computer vision, you need image processing experts to tell you what are the few (tens or hundreds) of most important features in an image. But if you use deep learning for computer vision, you just feed in the raw pixels, without caring much for image processing or feature extraction, which offers 20-30 per cent improvement in accuracy in most computer vision benchmarks.

For example, assume you saw a clear image of a dog, and assume that if the pixels of the image were modified just a few per cent, it is still easily recognised that there is a dog in the image. This is how deep learning works.

There are other key characteristics of deep learning that make it different from machine learning including:

Deep learning’s sophisticated technology and self-learning capabilities result in higher accuracy and faster processing. The technology can then learn high-level, non-linear features necessary for accurate classification.

Raw data is fed through deep neural networks, which learns to identify the object on which it is trained, per the example above.

As reference, deep neural networks are the first family of algorithms within machine learning that do not require manual feature engineering, rather, they learn on their own by processing and learning the high-level features from raw data.

Overall, machine learning has laid down the fundamental groundwork for deep learning to evolve. Deep learning has taken key features from the machine learning model, and takes it one step further by constantly teaching itself new abilities and adjusting existing ones.

Each model serves a significant purpose in today’s technology-based world, and it will be exciting to see how this technology will continue to evolve in new applications that will be present in our daily lives. Understanding the fundamental differences between machine learning and deep learning is valuable in knowing where it will lead us next.